Hybrid energy efficient deep learning and redactable consortium blockchain-based secure framework for smart parking in smart cities

Management of vehicular parking in the crowded environment is the indispensable requirement for the smart city scenario. The advent and potential development of Information Communication Technologies (ICT) and Internet of things (IoT) helped in proper thinking, operation and planning of parking management in smart city environment. But they suffer from the issue of privacy, security, integrity, energy efficiency, communication bandwidth and centralization. In this paper, Hybrid Deep learning and Redactable consortium blockchain-based Secure Framework (HDLRCBSF) is proposed for achieving the objective of energy-efficient smart parking in smart cities. This HDLRCBSF adopted the merits of Deep Bi-LSTM network for exploring the data associated with parking zones, such that optimal parking zones can be potentially allocated to the drivers with the ideal timing and location. It used the Redactable consortium blockchain network using Road Side Units (RSUs) which helped in attaining data verification and authentication at the security layer. It also implemented Threshold Chameleon Hash (TCH) algorithm for the objective of encrypting and decrypting the data related to the parking zones to establish secure communication. It adopted the significance of Deep Auto Regression Feature Augmented Bi-LSTM Networks in the analysis layer for exploring the cloud data that provides optimal parking lot to the drivers depending on the requirements that include cost, distance, and time. The quantitative and qualitative investigation conducted on the proposed HDLRCBSF confirmed its efficacy in terms of best parking space allocation rate of 21.32%, better than the baseline approaches that offered to the drivers with maximized privacy and security. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.